<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0123-3033</journal-id>
<journal-title><![CDATA[Ingeniería y competitividad]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. compet.]]></abbrev-journal-title>
<issn>0123-3033</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad del Valle]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-30332022000200006</article-id>
<article-id pub-id-type="doi">10.25100/iyc.v24i2.11352</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An approach to edge detection in medical imaging through histogram analysis and morphological gradient]]></article-title>
<article-title xml:lang="es"><![CDATA[Una aproximación a la detección de bordes en imágenes médicas mediante análisis de histograma y gradiente morfológico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Niño-Rondón]]></surname>
<given-names><![CDATA[Carlos V.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castellano-Carvajal]]></surname>
<given-names><![CDATA[Diego A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro-Casadiego]]></surname>
<given-names><![CDATA[Sergio A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Medina-Delgado]]></surname>
<given-names><![CDATA[Byron]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guevara-Ibarra]]></surname>
<given-names><![CDATA[Dinael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Francisco de Paula Santander Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[San José de Cúcuta ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-30332022000200006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-30332022000200006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-30332022000200006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Edge detection takes importance in image processing systems for computer-aided diagnosis, where sharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions of interest to the specialist. A method for feature enhancement and edge detection in medical images was developed using image processing by analyzing the pixel distribution histogram and morphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT dataset were used. The method is based on image processing and is applied to mammography and chest CT images, where blur filtering steps are accompanied by morphological gradient filtering, in addition to obtaining the threshold for edge detection by analyzing the point of maximum pixel concentration according to the distribution histogram. The processing is presented in a graphical user interface developed in Python language. The method is validated by comparison with other edge detection techniques such as the Canny Algorithm, and with deep learning methods such as Holistically-Nested Edge Detection. The proposed method improves image quality in both mammograms and CT scans compared to other techniques. It also presents the best performance considering internal and external edge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aided diagnosis processes due to its high efficiency in edge detection.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La detección de bordes toma importancia en los sistemas de procesamiento de imágenes para el diagnóstico asistido por ordenador, donde se analizan los cambios bruscos en la intensidad de los píxeles para obtener información rápida y precisa sobre las regiones de interés para el especialista. Se desarrolló un método para el realce de caracteristicas y detección de bordes en imágenes médicas utilizando procesamiento de imágenes analizando el histograma de distribución de píxeles y la operación de gradiente morfológico. Se utilizaron imágenes del conjunto de datos MINI MIAS y del conjunto de datos COVID-CT. El método se basa en procesamiento de imágenes y se aplica a las imágenes de mamografía y TAC de tórax, donde los pasos de filtrado de desenfoque se acompañan de filtrado de gradiente morfológico, además de obtener el umbral para detectar el borde mediante el análisis del punto de máxima concentración de píxeles según el histograma de distribución. El procesamiento se presenta en una interfaz gráfica de usuario desarrollada en lenguaje Python. El método se valida mediante la comparación con otras técnicas de detección de bordes como el Algoritmo Canny, y con métodos de aprendizaje profundo como el Holistically-Nested Edge Detection. El método propuesto mejora la calidad de la imagen tanto en mamografías como en TAC en comparación con otras técnicas. También presenta el mejor rendimiento teniendo en cuenta la detección de bordes internos y externos, así como un tiempo medio de respuesta de 1.054 segundos y 2.63 % de requerimiento de la Unidad Central de Procesamiento. El sistema desarrollado se presenta como una herramienta de apoyo para su uso en procesos de diagnóstico asistido por ordenador debido a su alta eficiencia en la detección de bordes.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[computer aided diagnosis]]></kwd>
<kwd lng="en"><![CDATA[computerized tomography]]></kwd>
<kwd lng="en"><![CDATA[edge detection]]></kwd>
<kwd lng="en"><![CDATA[Image processing]]></kwd>
<kwd lng="en"><![CDATA[mammography]]></kwd>
<kwd lng="es"><![CDATA[detección de bordes]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico asistido por computador]]></kwd>
<kwd lng="es"><![CDATA[mamografía]]></kwd>
<kwd lng="es"><![CDATA[procesamiento de imágenes]]></kwd>
<kwd lng="es"><![CDATA[tomografía computarizada]]></kwd>
</kwd-group>
</article-meta>
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